Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy

Abstract

In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.

Keywords: Cell tracking; Circular Hough transform; Level-set methods; Mitosis analysis; Phase contrast microscopy; Variational methods.

Fig. 1

Common image characteristics in phase contrast microscopy: shade-off effect (a) and halo effect (b) (HeLa DMSO control cells).

Fig. 2

The circular Hough transform.

Fig. 3

Level-set function.

Fig. 4

MitosisAnalyser MATLAB®GUI.

Fig. 5

Summary of MitosisAnalyser framework.

Fig. 6

Finding circles by means of the CHT. From left to right: Original greyscale image, gradient image, edge pixels, accumulator matrix, transformed matrix.

Fig. 7

Level-set evolution from initialisation to final iteration.

Fig. 8

Three examples of mitotic events detected for FUCCI MIA PaCa-2 “DMSO control”, “treatment with 3 nM paclitaxel” and “treatment with 30 nM paclitaxel” data (from top to bottom).

Fig. 9

Five examples of mitotic events detected for HeLa Aur A “DMSO control” (one each in row one and two), “treatment with 25 nM MLN8237” (one each in row three and four), and “combined treatment with 25 nM MLN8237 and 0.75 nM paclitaxel” (bottom row) data.

Fig. 10

Three manually segmented classes of T24 cells: apoptotic (top row), flat/normal (middle row) and mitotic (bottom row).

Fig. 11

Key features for cell type classification.

Fig. 12

Boxplots showing JSC (left) and MHD (right) measures for segmentation of apoptotic cell images by MitosisAnalyser (MiA), the model by Chan and Vese (CV) and geodesic active contours (GAC) in comparison with manual segmentation.

Fig. 13

Exemplary segmentations for flat cells in phase contrast images: Manual segmentation (magenta) is compared to performance of MitosisAnalyser (cyan). The average JSC and MHD values for the four images are 0.8377 and 0.3648, respectively.